Proposes a Markov transform and estimator for the covariance kernel in functional data under Markov constraints, with a new test for the Markov property in continuous graphical models.
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2026 2verdicts
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Integrates partial ODE physics into SDE-based causal discovery via drift-diffusion separation, with sparsity-inducing quasi-likelihood estimation, recovery guarantees for stable/unstable systems, and robustness analysis to model misspecification.
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Inference for Functional Data under Markov Constraints
Proposes a Markov transform and estimator for the covariance kernel in functional data under Markov constraints, with a new test for the Markov property in continuous graphical models.
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Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
Integrates partial ODE physics into SDE-based causal discovery via drift-diffusion separation, with sparsity-inducing quasi-likelihood estimation, recovery guarantees for stable/unstable systems, and robustness analysis to model misspecification.